LLM Thematic Generalization Benchmark
Thematic Generalization Benchmark: measures how effectively various LLMs can infer a narrow or specific "theme" (category/rule) from a small set of examples and anti-examples, then detect which item truly fits that theme among a collection of misleading candidates. - lechmazur/generalization
LLM Thematic Generalization Benchmark launched on January 14, 2025 with 6 votes and more than 75% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
This benchmark tests whether large language models can infer a specific latent theme from a few examples, use anti-examples to reject the broader but wrong pattern, and then identify the one true match among close distractors. Models score all 8 candidates. We then measure whether the correct candidate is ranked first, and how highly it tends to be ranked overall. V2 uses 1,247 validated prompts and adds stricter ambiguity filtering plus harder evaluation slices. The previous published release is being archived as V1. The chart uses inverse-rank score , a higher-is-better transformation of the average rank assigned to the correct answer. It is easier to interpret than raw average rank and…from github.com
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